#tensorflow

Build an Abstractive Text Summarizer in 94 Lines of #tensorflow !! (Tutorial 6)
▻https://hackernoon.com/build-an-abstractive-text-summarizer-in-94-lines-of-tensorflow-tutorial-

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Build an Abstractive Text Summarizer in 94 Lines of Tensorflow !! (Tutorial 6)This tutorial is the sixth one from a series of tutorials that would help you build an abstractive text summarizer using tensorflow , today we would build an abstractive text summarizer in tensorflow in an optimized way .Today we would go through one of the most optimized models that has been built for this task , this model has been written by dongjun-Lee , this is the link to his model , I have used his model model on different datasets (in different languages) and it resulted in truly amazing results , so I would truly like to thank him for his effortI have made multiple modifications to the model to enable it to enable it to run seamlessly on google colab (link to my model) , and i have hosted the data onto (...)

#machine-learning #nlp #ai #deep-learning

How we improved #tensorflow Serving #performance by over 70%
▻https://hackernoon.com/how-we-improved-tensorflow-serving-performance-by-over-70-f21b5dad2d98?s

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Tensorflow has grown to be the de facto ML platform, popular within both industry and research. The demand and support for Tensorflow has contributed to host of OSS libraries, tools and frameworks around training and serving ML models. The Tensorflow Serving is a project built to focus on the inference aspect for serving ML models in a distributed, production environment.Mux uses Tensorflow Serving in several parts of its infrastructure, and we’ve previously discussed using Tensorflow Serving to power our per-title-encoding feature. Today, we’ll focus on techniques that improve latency by optimizing both the prediction server and client. Model predictions are usually “online” operations (on critical application request path), thus our primary optimization objectives are to handle high (...)

#docker #artificial-intelligence #machine-learning

#tensorflow is dead, long live TensorFlow!
▻https://hackernoon.com/tensorflow-is-dead-long-live-tensorflow-49d3e975cf04?source=rss----3a814

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If you’re an AI enthusiast and you didn’t see the big news this month, you might have just snoozed through an off-the-charts earthquake. Everything is about to change!What is this? The TensorFlow logo or the letter you use to answer tough True/False exam questions?Last year I wrote 9 Things You Need To Know About TensorFlow… but there’s one thing you need to know above all others: TensorFlow 2.0 is here!The revolution is here! Welcome to TensorFlow 2.0.It’s a radical makeover. The consequences of what just happened are going to have major ripple effects on every industry, just you wait. If you’re a TF beginner in mid-2019, you’re extra lucky because you picked the best possible time to enter AI (though you might want to start from scratch if your old tutorials have the word “session” in (...)

#technology #machine-learning #artificial-intelligence #hackernoon-top-story

A Quick Introduction to Artificial Intelligence, Machine Learning, Deep Learning and #tensorflow
▻https://hackernoon.com/a-quick-introduction-to-artificial-intelligence-machine-learning-deep-le

What used to be just a pipe dream in the realms of science fiction, artificial intelligence (AI) is now mainstream technology in our everyday lives with applications in image and voice recognition, language translations, chatbots, and predictive data analysis.In this article, we’ll introduce AI along with its related terms machine learning and deep learning. By the end of the article you should understand these terms, how things generally work and be more familiar with terms like Inception and YOLO (and no, we’re not talking about the Leonardo DiCaprio movie or some internet meme).Artificial intelligence (AI) is the simulation of human intelligence by computers. Machine learning is a branch of AI where algorithms are used to learn from data to make future decisions or predictions. Deep (...)

#open-source #artificial-intelligence #deep-learning #machine-learning

Handtrack.js: Hand Tracking Interactions in the Browser using #tensorflow.js and 3 lines of code.
▻https://hackernoon.com/handtrackjs-677c29c1d585?source=rss----3a8144eabfe3---4

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Handtrack.js: Hand Tracking Interactions in the Browser using Tensorflow.js and 3 lines of code.Handtrack.js library allows you track a user’s hand (bounding box) from an image in any orientation, in 3 lines of code.Here’s an example interface built using Handtrack.js to track hands from webcam feed. Try the demo here.Runtime: 22 FPS. On a Macbook Pro 2018, 2.2 Ghz, Chrome browser. 13 FPS on a Macbook Pro 2014 2.2GHz.A while ago, I was really blown away by results from an experiment using TensorFlow object detection api to track hands in an image. I made the trained model and source code available, and since then it has been used to prototype some rather interesting usecases (a tool to help kids spell, extensions to predict sign language, hand ping pong, etc). However, while many (...)

#towards-data-science #javascript #artificial-intelligence #machine-learning

Enterprise™ AF Solution for Text Classification (using BERT)
▻https://hackernoon.com/enterprise-af-solution-for-text-classification-using-bert-9fe2b7234c46?s

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What is BERT? How does one use BERT to solve problems? Google Colab, #tensorflow, #kubernetes on Google CloudOverviewThis for people who want to create a REST service using a model built with BERT, the best NLP base model available. I spent a lot of time figuring out how to put a solution together so I figured I would write up how to deploy a solution and share!Why should you read this?Today we have machine learning engineers, software engineers, and data scientists. The trend in deep learning is that models are getting so powerful that there is little need to know about the details of the specific algorithm and can be immediately applied to custom use cases. This trend will turn the job of machine learning engineers into a skill that software engineers have. There will still be data (...)

#machine-learning #artificial-intelligence #enterprise-af

Transfer Learning : Approaches and Empirical Observations
▻https://hackernoon.com/transfer-learning-approaches-and-empirical-observations-efeff9dfeca6?sou

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Transfer Learning : Approaches and Empirical InsightsIf data is currency, then transfer learning is a messiah for the poors▻https://medium.com/media/868569aa242986fcdf8e6551d15f791e/hrefWhile there is no dearth of learning resources on this topic, only a few of them could couple the theoretical and empirical parts together and be intuitive enough. The reason ?? I guess we don’t transfer the knowledge in the exact way we store it in our minds. I believe that presenting complex topics in simple ways is an art, so lets master it.Lets begin a series of blogs where we will try to discuss why the things are the way they are and the intuitions behind them in the domain of machine learning. The first in the line is Transfer Learning (TL). We will begin with a crisp intro about TL followed by (...)

#deep-learning #data-science #transfer-learning #tensorflow #machine-learning

Implementing a Sequence-to-Sequence Model
▻https://hackernoon.com/implementing-a-sequence-to-sequence-model-45a6133958ca?source=rss----3a8

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Learn how to implement a sequence-to-sequence model in this article by Matthew Lamons, founder, and CEO of Skejul — the AI platform to help people manage their activities, and Rahul Kumar, an AI scientist, deep learning practitioner, and independent researcher.In this article, you’ll implement a seq2seq model (an encoder-decoder RNN) for a simple sequence-to-sequence question-answer task. This model can be trained to map an input sequence (questions) to an output sequence (answers), which are not necessarily of the same length as each other.This type of seq2seq model has shown impressive performance in various other tasks such as speech recognition, machine translation, question answering, Neural Machine Translation (NMT), and image caption generation.The following diagram helps you (...)

#keras #python #deep-learning #tensorflow #machine-learning

Logistic Regression with #tensorflow and #keras
▻https://hackernoon.com/logistic-regression-with-tensorflow-and-keras-83d2487aed89?source=rss---

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Learn logistic regression with TensorFlow and Keras in this article by Armando Fandango, an inventor of AI empowered products by leveraging expertise in deep learning, machine learning, distributed computing, and computational methods. He has also provided thought leadership roles as Chief Data Scientist and Director at startups and large enterprises.This article will show you how to implement a classification algorithm, known as multinomial logistic regression, to identify the handwritten digits #dataset. You’ll use both TensorFlow core and Keras to implement this logistic regression algorithm.Logistic regression with TensorFlowOne of the most popular examples regarding multiclass classification is to label the images of handwritten digits. The classes, or labels, in this example are (...)

#machine-learning #logistic-regression

#tensorflow.js — Real-Time Object Detection in 10 Lines of Code
▻https://hackernoon.com/tensorflow-js-real-time-object-detection-in-10-lines-of-code-baf15dfb95b

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TensorFlow.js — Real-Time Object Detection in 10 Lines of CodeIn my last article I showed you how to do image classification in the browser.Image classification can be a very useful tool, it can give us an idea of what’s in an image. However, sometimes we want more. It can be a little counterintuitive, but just because a machine learning model can tell what’s in an image, doesn’t mean it can tell us where it is in the image. We need a different architecture for that.That’s where object detection comes into play.Object detection opens up the capability of counting how many objects are in a scene, tracking motion and simply just locating an object’s position.By the end of this tutorial we’ll have a fully functional real-time object detection web app that will track objects via our webcam.Detecting (...)

#machine-learning #technology #javascript #artificial-intelligence

Multi-GPU training with Brain Builder and #tensorflow
▻https://hackernoon.com/multi-gpu-training-with-brain-builder-and-tensorflow-3b7aba2eb84b?source

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Yes, building AI is hard! Every step from data annotation, training and deployment comes with its own set of challenges. This blog post will try to deal with the first two of those:Data AnnotationTrainingOver the past few years at Neurala, we have been developing highly efficient AI systems deployed in millions of consumer devices as of today! And in this process, we developed a lot of tools to simplify our workflow. We have finally decided to bridge the AI skills gap by making some of our tools public. One of the tools, Brain Builder deals with the first of the aforementioned issues: Data Annotation.Brain Builder is an AI-assisted annotation tool that fits seamlessly into commonly used frameworks like TensorFlow and Caffe. This post will walk you through the steps you’d need to (...)

#convolutional-network #deep-learning #machine-learning #data-science

Start Using #tensorflow.js — Without Leaving This Article
▻https://hackernoon.com/start-using-tensorflow-js-without-leaving-this-article-fb683ac509ed?sour

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Start Using TensorFlow.js — Without Leaving This ArticleOne of the largest obstacles for beginners getting experience with artificial intelligence and machine learning can honestly be the setup.I’m not going to lie, there are still plenty of days that completely slip away, just trying to get Python, TensorFlow and my GPU to cooperate. Does this make me question my abilities as a competent software engineer? Yes, yes it does.I digress.So what is TensorFlow.js and how can it help us? From the official page, TensorFlow.js is, “A #javascript library for training and deploying ML models in the browser and on Node.js.”What does that mean for us? We can try it out right from this Medium article!▻https://medium.com/media/da56293a024508102e9c1b92fa4b7de5/hrefIn this demo we are using a deep learning model (...)

#artificial-intelligence #machine-learning #tensorflowjs

MOVE MIRROR, by Jane Friedhoff & Irene Alvarado
▻https://experiments.withgoogle.com/collection/ai/move-mirror/view

An AI Experiment which matches your pose with a catalogue of 80,000 photos while you move

▻https://www.youtube.com/watch?v=JvzkFJW6LIU

trouvé sur ▻https://aijs.rocks

#danse #machine-learning #posenet #ml #tensorflow

Forecasting Market Movements Using #tensorflow
▻https://hackernoon.com/forecasting-market-movements-using-tensorflow-fb73e614cd06?source=rss---

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Photo by jesse orrico on UnsplashMulti-Layer Perceptron for ClassificationIs it possible to create a neural network for predicting daily market movements from a set of standard trading indicators?In this post we’ll be looking at a simple model using Tensorflow to create a framework for testing and development, along with some preliminary results and suggested improvements.The ML Task and Input FeaturesTo keep the basic design simple, it’s setup for a binary classification task, predicting whether the next day’s close is going to be higher or lower than the current, corresponding to a prediction to either go long or short for the next time period. In reality, this could be applied to a bot which calculates and executes a set of positions at the start of a trading day to capture the day’s (...)

#forecasting-tensorflow #machine-learning #market-movement

#tensorflow Vs #keras? — Comparison by building a model for image classification.
▻https://hackernoon.com/tensorflow-vs-keras-comparison-by-building-a-model-for-image-classificat

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Yes , as the title says , it has been very usual talk among data-scientists (even you!) where a few say , TensorFlow is better and some say Keras is way good! Let’s see how this thing actually works out in practice in the case of image classification.Before that let’s introduce these two terms Keras and Tensorflow and help you build a powerful image classifier within 10 min!Tensorflow:Tensorflow is the most used library to develop models in deep learning. It has been the best ever library which has been completely opted by many geeks in their daily experiments . Could you imagine if I say that Google has put Tensor Processing Units (TPU) just to deal with tensors ? Yes, they have. They have put a separate class of instances called TPU which has the most power driven computational power to (...)

#tensorflow-vs-keras #image-classification #machine-learning

Introduction of #tensorflow with Python
▻https://hackernoon.com/introduction-of-tensorflow-with-python-f4a9624f2ab2?source=rss----3a8144

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Photo on UnsplashMachine learning is the most popular part of our technology world. TensorFlow is also a popular open source and it is a framework of deep learning. The Deep Learning is part of Machine Learning.Photo on UnsplashDeep learning is also a large part of machine learning methods based on learning data presentations — “as opposed to task-specific algorithms.”Photo on @GoogleWhat is TensorFlow?It’s a framework to perform computation very efficiently, and it can tap into the GPU (Graphics Processor Unit) in order too speed it up even further. This will make a huge effect as we shall see shortly. TensorFlow can be controlled by a simple Python API, which we will be using in this Article.Graphs and TensorsWhen a native computation is done in many programming languages, it is usually (...)

#matplotlib #python3 #machine-learning #deep-learning

Deep Learning for Recommendation with Keras and TensorRec
▻https://hackernoon.com/deep-learning-for-recommendation-with-keras-and-tensorrec-2b8935c795d0?s

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With the release of TensorRec v0.21, I’ve added the ability to easily use deep neural networks in your recommender system.For some recommender problems, such as cold-start recommendation problems, deep learning can be an elegant solution for learning from user and item metadata. Using TensorRec with Keras, you can now experiment with deep representation models in your recommender systems quickly and easily.ImplementationIn a TensorRec model, the components that learn how to process user and item features are called the “representation graphs” (or “repr” for short). These graphs convert high-dimensional user/item features, such as metadata and indicator variables, into low-dimensional user/item representations.The TensorRec recommender system.Define your model’s representation graphs as a (...)

#deep-learning #tensorflow #machine-learning #data-science #recommendation-system

Google’s AI is being used by US military drone programme (https://w...
▻https://diasp.eu/p/6850146

Google’s AI is being used by US military drone programme | #google #AI #ArtificialIntelligence #maven #drone #tensorflow

What to do when you have bad realtime train position data from the railways company ? Plant cameras along the tracks and use image recognition to discriminate trains and refine arrival predictions: ▻http://svds.com/tensorflow-image-recognition-raspberry-pi

And what did they use for training data ? Trains, of course.

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Google uses its own deep learning chip for artificial intelligence (AI)

▻https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chi

Tensor Processing Unit (TPU), a custom ASIC we built specifically for machine learning — and tailored for #TensorFlow.

We’ve been running TPUs inside our data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning.

[...]

TPU is tailored to machine learning applications, allowing the chip to be more tolerant of reduced computational precision, which means it requires fewer transistors per operation. Because of this, we can squeeze more operations per second into the silicon, use more sophisticated and powerful machine learning models and apply these models more quickly, so users get more intelligent results more rapidly. A board with a TPU fits into a hard disk drive slot in our data center racks.

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TPUs already power many applications at Google, including RankBrain, used to improve the relevancy of search results and Street View, to improve the accuracy and quality of our maps and navigation. AlphaGo was powered by TPUs in the matches against Go world champion, Lee Sedol, enabling it to “think” much faster and look farther ahead between moves.

Tinker With a Neural Network Right Here in Your Browser.

A #Neural_Network Playground
▻http://playground.tensorflow.org

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